Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "223" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 72 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 70 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459888 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.499893 | 1.983262 | -1.863638 | 0.478789 | -0.806022 | 120.239409 | 0.112898 | 7.619829 | 0.6598 | 0.5832 | 0.4158 | nan | nan |
| 2459887 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.451377 | 1.065065 | -2.272165 | -0.187833 | -1.601210 | 28.031132 | 0.839195 | 5.258536 | 0.6470 | 0.6632 | 0.3962 | nan | nan |
| 2459886 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.263292 | 1.750142 | -1.710911 | 1.054206 | -1.033550 | 0.591278 | -1.519534 | -0.135005 | 0.7460 | 0.7453 | 0.3303 | nan | nan |
| 2459885 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.179063 | 3.364107 | 11.619251 | 21.525591 | 2.130391 | 47.033431 | 4.855287 | 7.057779 | 0.6955 | 0.7029 | 0.3517 | nan | nan |
| 2459884 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.679466 | 0.562915 | -1.679853 | -0.637942 | -0.714419 | -2.156045 | 0.104260 | 5.451845 | 0.6441 | 0.6284 | 0.3799 | nan | nan |
| 2459883 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.131763 | 3.223867 | 9.639888 | 21.458560 | 0.808554 | 21.870813 | 4.295507 | 8.398476 | 0.6442 | 0.6685 | 0.3864 | nan | nan |
| 2459882 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.256494 | 4.707967 | 11.801986 | 17.474381 | 2.009584 | 1.606922 | 1.462044 | 6.539836 | 0.6452 | 0.6589 | 0.3731 | nan | nan |
| 2459881 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.735102 | 2.522607 | 13.249360 | 21.222908 | 1.574350 | 2.511546 | 1.806760 | 19.820373 | 0.6885 | 0.7175 | 0.3254 | nan | nan |
| 2459880 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.844028 | 3.417368 | 10.059686 | 20.147360 | 1.056169 | 19.586060 | 3.129142 | 5.157858 | 0.6408 | 0.6675 | 0.3897 | nan | nan |
| 2459879 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.394976 | 0.230575 | -2.029826 | -2.459393 | -1.657796 | -0.990101 | -0.049487 | 2.652211 | 0.6328 | 0.6618 | 0.3935 | nan | nan |
| 2459878 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.475470 | 2.900921 | 12.392118 | 8.392529 | 1.704979 | 55.440779 | 2.441647 | 9.483590 | 0.6365 | 0.6457 | 0.3850 | nan | nan |
| 2459839 | RF_ok | 100.00% | - | - | - | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459830 | RF_ok | 100.00% | 59.68% | 59.68% | 0.00% | 100.00% | 0.00% | 25.730793 | 26.448830 | 9.159617 | 16.020409 | 25.266025 | 26.802111 | -3.948424 | -5.349202 | 0.3220 | 0.2252 | 0.2145 | 1.146704 | 1.130210 |
| 2459829 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 30.283107 | 29.633971 | 7.225815 | 14.713322 | 1.418887 | 7.000411 | -0.157390 | -2.328024 | 0.7154 | 0.6216 | 0.4407 | 0.000000 | 0.000000 |
| 2459828 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 21.769713 | 22.988952 | 5.805177 | 11.795964 | 7.545788 | 14.795476 | -1.138375 | -5.013404 | 0.7843 | 0.4976 | 0.5747 | 0.000000 | 0.000000 |
| 2459827 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 23.145621 | 22.741825 | 10.131740 | 14.868535 | 2.585416 | 49.178132 | 0.151839 | 1.117565 | 0.7292 | 0.6253 | 0.4345 | 0.000000 | 0.000000 |
| 2459826 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 19.583151 | 18.765016 | 8.781306 | 4.160384 | 9.797718 | 3.511396 | -0.995015 | 46.952622 | 0.7779 | 0.4975 | 0.5519 | 0.000000 | 0.000000 |
| 2459825 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 21.420887 | 21.695435 | 6.479094 | 1.255757 | 6.191974 | 3.139554 | -0.109553 | 10.434819 | 0.7741 | 0.4815 | 0.5597 | 4.296859 | 2.879089 |
| 2459824 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 19.371260 | 19.422573 | 8.653841 | 5.349106 | -0.651193 | 35.119398 | -0.200898 | 15.962619 | 0.6866 | 0.6594 | 0.3909 | 0.000000 | 0.000000 |
| 2459823 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 18.190594 | 17.510864 | 7.877900 | 5.921908 | 10.536313 | 8.302082 | 1.644926 | 41.910143 | 0.7362 | 0.5668 | 0.4932 | 12.712826 | 7.889633 |
| 2459822 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 20.220042 | 19.438131 | 7.916030 | 5.977450 | 7.487719 | 4.304763 | 0.058736 | 2.913902 | 0.7742 | 0.5439 | 0.5294 | 4.310445 | 3.027539 |
| 2459821 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 22.924252 | 26.307396 | 7.887223 | 15.136018 | 5.463106 | 12.418022 | -0.880558 | -1.616570 | 0.7605 | 0.5524 | 0.5311 | 3.543463 | 2.900312 |
| 2459820 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 23.949613 | 23.807133 | 8.404231 | 15.974579 | 8.937738 | 23.833746 | 0.070304 | -1.105808 | 0.7377 | 0.6333 | 0.4478 | 5.371805 | 4.010229 |
| 2459817 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459816 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 17.754964 | 17.623005 | 9.290439 | 8.289445 | 14.535029 | 12.469759 | -0.707992 | 8.151835 | 0.8281 | 0.5373 | 0.6263 | 4.172725 | 3.144267 |
| 2459815 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 18.847416 | 18.556090 | 7.456217 | 7.101463 | 13.668889 | 12.033236 | 0.143735 | 4.432160 | 0.7557 | 0.5995 | 0.5318 | 3.619625 | 3.021642 |
| 2459814 | RF_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Variability | 120.239409 | 1.983262 | 0.499893 | 0.478789 | -1.863638 | 120.239409 | -0.806022 | 7.619829 | 0.112898 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Variability | 28.031132 | 1.065065 | 0.451377 | -0.187833 | -2.272165 | 28.031132 | -1.601210 | 5.258536 | 0.839195 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Shape | 1.750142 | 0.263292 | 1.750142 | -1.710911 | 1.054206 | -1.033550 | 0.591278 | -1.519534 | -0.135005 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Variability | 47.033431 | 3.364107 | 5.179063 | 21.525591 | 11.619251 | 47.033431 | 2.130391 | 7.057779 | 4.855287 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Discontinuties | 5.451845 | 0.562915 | 1.679466 | -0.637942 | -1.679853 | -2.156045 | -0.714419 | 5.451845 | 0.104260 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Variability | 21.870813 | 3.223867 | 4.131763 | 21.458560 | 9.639888 | 21.870813 | 0.808554 | 8.398476 | 4.295507 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Power | 17.474381 | 4.707967 | 7.256494 | 17.474381 | 11.801986 | 1.606922 | 2.009584 | 6.539836 | 1.462044 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Power | 21.222908 | 2.522607 | 3.735102 | 21.222908 | 13.249360 | 2.511546 | 1.574350 | 19.820373 | 1.806760 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Power | 20.147360 | 3.417368 | 4.844028 | 20.147360 | 10.059686 | 19.586060 | 1.056169 | 5.157858 | 3.129142 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Discontinuties | 2.652211 | 0.230575 | 1.394976 | -2.459393 | -2.029826 | -0.990101 | -1.657796 | 2.652211 | -0.049487 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Variability | 55.440779 | 2.900921 | 4.475470 | 8.392529 | 12.392118 | 55.440779 | 1.704979 | 9.483590 | 2.441647 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Variability | 26.802111 | 25.730793 | 26.448830 | 9.159617 | 16.020409 | 25.266025 | 26.802111 | -3.948424 | -5.349202 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | ee Shape | 30.283107 | 29.633971 | 30.283107 | 14.713322 | 7.225815 | 7.000411 | 1.418887 | -2.328024 | -0.157390 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Shape | 22.988952 | 22.988952 | 21.769713 | 11.795964 | 5.805177 | 14.795476 | 7.545788 | -5.013404 | -1.138375 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Variability | 49.178132 | 23.145621 | 22.741825 | 10.131740 | 14.868535 | 2.585416 | 49.178132 | 0.151839 | 1.117565 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Discontinuties | 46.952622 | 18.765016 | 19.583151 | 4.160384 | 8.781306 | 3.511396 | 9.797718 | 46.952622 | -0.995015 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Shape | 21.695435 | 21.695435 | 21.420887 | 1.255757 | 6.479094 | 3.139554 | 6.191974 | 10.434819 | -0.109553 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Variability | 35.119398 | 19.371260 | 19.422573 | 8.653841 | 5.349106 | -0.651193 | 35.119398 | -0.200898 | 15.962619 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Discontinuties | 41.910143 | 17.510864 | 18.190594 | 5.921908 | 7.877900 | 8.302082 | 10.536313 | 41.910143 | 1.644926 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | ee Shape | 20.220042 | 20.220042 | 19.438131 | 7.916030 | 5.977450 | 7.487719 | 4.304763 | 0.058736 | 2.913902 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Shape | 26.307396 | 26.307396 | 22.924252 | 15.136018 | 7.887223 | 12.418022 | 5.463106 | -1.616570 | -0.880558 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | ee Shape | 23.949613 | 23.949613 | 23.807133 | 8.404231 | 15.974579 | 8.937738 | 23.833746 | 0.070304 | -1.105808 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | ee Shape | 17.754964 | 17.623005 | 17.754964 | 8.289445 | 9.290439 | 12.469759 | 14.535029 | 8.151835 | -0.707992 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | ee Shape | 18.847416 | 18.556090 | 18.847416 | 7.101463 | 7.456217 | 12.033236 | 13.668889 | 4.432160 | 0.143735 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |